{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "09_Data_and_Models",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "bOChJSNXtC9g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data and Models"
      ]
    },
    {
      "metadata": {
        "id": "OLIxEDq6VhvZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/logo.png\" width=150>\n",
        "\n",
        "So far we've seen a variety of different models on different datasets for different tasks (regression/classification) and we're going to learn about even more algorithms in subsequent lessons. But we've ignored a fundamental concept about data and modeling: quality and quantity. In a nutshell, a machine learning model consumes input data and produces predictions. The quality of the predictions directly corresponds to the quality and quantity of data you train the model with; garbage in, garbage out.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/nutshell.png\" width=500>\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "FLH7kzZl8wnf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Set Up"
      ]
    },
    {
      "metadata": {
        "id": "qAE9BjMH8x4q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We're going to go through all the concepts with concrete code examples. We'll first synthesize some data to train our models on. The task is to determine whether a tumor will be benign (harmless) or malignant (harmful) based on leukocyte (white blood cells) count and blood pressure. "
      ]
    },
    {
      "metadata": {
        "id": "m0nzLDcVXJTx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load PyTorch library\n",
        "!pip3 install torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "N9uu2nngKDrW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from argparse import Namespace\n",
        "import collections\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gPHmsndLdUOH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Set Numpy and PyTorch seeds\n",
        "def set_seeds(seed, cuda):\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    if cuda:\n",
        "        torch.cuda.manual_seed_all(seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0-dXQiLlTIgz",
        "colab_type": "code",
        "outputId": "d525f333-8024-48d3-ab6d-ea9507b9a94a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Arguments\n",
        "args = Namespace(\n",
        "    seed=1234,\n",
        "    cuda=False,\n",
        "    shuffle=True,\n",
        "    data_file=\"tumors.csv\",\n",
        "    reduced_data_file=\"tumors_reduced.csv\",\n",
        "    train_size=0.75,\n",
        "    test_size=0.25,\n",
        "    num_hidden_units=100,\n",
        "    learning_rate=1e-3,\n",
        "    num_epochs=100,\n",
        ")\n",
        "\n",
        "# Check CUDA\n",
        "if not torch.cuda.is_available():\n",
        "    args.cuda = False\n",
        "args.device = torch.device(\"cuda\" if args.cuda else \"cpu\")\n",
        "print(\"Using CUDA: {}\".format(args.cuda))\n",
        "\n",
        "# Set seeds\n",
        "set_seeds(seed=args.seed, cuda=args.cuda)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using CUDA: False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RV2IddoZde-r",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data"
      ]
    },
    {
      "metadata": {
        "id": "5wDazzQdaoy2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import re\n",
        "import urllib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GbsXoFVgdh6K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Upload data from GitHub to notebook's local drive\n",
        "url = \"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/data/tumors.csv\"\n",
        "response = urllib.request.urlopen(url)\n",
        "html = response.read()\n",
        "with open(args.data_file, 'wb') as fp:\n",
        "    fp.write(html)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y6LNWmoidh8q",
        "colab_type": "code",
        "outputId": "a2ee99a4-8c89-41d4-d482-7e098b097d14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# Raw data\n",
        "df = pd.read_csv(args.data_file, header=0)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>leukocyte_count</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>tumor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.472969</td>\n",
              "      <td>15.250393</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.805510</td>\n",
              "      <td>14.109676</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.834053</td>\n",
              "      <td>15.793920</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.572811</td>\n",
              "      <td>17.873286</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.633667</td>\n",
              "      <td>16.598559</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   leukocyte_count  blood_pressure  tumor\n",
              "0        13.472969       15.250393      1\n",
              "1        10.805510       14.109676      1\n",
              "2        13.834053       15.793920      1\n",
              "3         9.572811       17.873286      1\n",
              "4         7.633667       16.598559      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "YVo6CuZLC3h2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_tumors(df):\n",
        "    i = 0; colors=['r', 'b']\n",
        "    for name, group in df.groupby(\"tumor\"):\n",
        "        plt.scatter(group.leukocyte_count, group.blood_pressure, edgecolors='k',\n",
        "                   color=colors[i]); i += 1\n",
        "    plt.xlabel('leukocyte count')\n",
        "    plt.ylabel('blood pressure')\n",
        "    plt.legend(['0 - benign', '1 - malignant'], loc=\"upper right\")\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nXFUmnfte6z6",
        "colab_type": "code",
        "outputId": "2b797875-3e15-4e67-ed8c-1585e9e54ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot data\n",
        "plot_tumors(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SSeBIoy2RtCItYbDcy+aSEevY3gTaVjHcrRnbW624RNKaSKMtkbQwrl51Sxms/Pzchtr1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\nBAKBIET4/y8q7wsoT6NxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4f87b70978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "237OzHqlNQ-D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Convert to PyTorch tensors\n",
        "X = df.as_matrix(columns=['leukocyte_count', 'blood_pressure'])\n",
        "y = df.as_matrix(columns=['tumor'])\n",
        "X = torch.from_numpy(X).float()\n",
        "y = torch.from_numpy(y.ravel()).long()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0pahDv9WLD2S",
        "colab_type": "code",
        "outputId": "e3e7a63a-e027-4d04-8c54-8d658419750d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Shuffle data\n",
        "shuffle_indices = torch.LongTensor(random.sample(range(0, len(X)), len(X)))\n",
        "X = X[shuffle_indices]\n",
        "y = y[shuffle_indices]\n",
        "\n",
        "# Split datasets\n",
        "test_start_idx = int(len(X) * args.train_size)\n",
        "X_train = X[:test_start_idx] \n",
        "y_train = y[:test_start_idx] \n",
        "X_test = X[test_start_idx:] \n",
        "y_test = y[test_start_idx:]\n",
        "print(\"We have %i train samples and %i test samples.\" % (len(X_train), len(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 750 train samples and 250 test samples.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "owLnzReJJdpj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Model"
      ]
    },
    {
      "metadata": {
        "id": "zlPe1lXEJfcA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's fit a model on this synthetic data."
      ]
    },
    {
      "metadata": {
        "id": "0WhYfDOjJdIV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nTtsFHZY_G45",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Multilayer Perceptron \n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        a_1 = F.relu(self.fc1(x_in)) # activaton function added!\n",
        "        y_pred = self.fc2(a_1)\n",
        "\n",
        "        if apply_softmax:\n",
        "            y_pred = F.softmax(y_pred, dim=1)\n",
        "\n",
        "        return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1kXlfHpPJ5Vq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=len(df.columns)-1, \n",
        "            hidden_dim=args.num_hidden_units, \n",
        "            output_dim=len(set(df.tumor)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ncxbef0yJ6pD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "srlaBr8oiftE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Accuracy\n",
        "def get_accuracy(y_pred, y_target):\n",
        "    n_correct = torch.eq(y_pred, y_target).sum().item()\n",
        "    accuracy = n_correct / len(y_pred) * 100\n",
        "    return accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Mjg4u-zCK90q",
        "colab_type": "code",
        "outputId": "cdcefbe6-fbdf-4cfb-de36-d365573d2ed5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "    \n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%20==0: \n",
        "        print (\"epoch: {0:02d} | loss: {1:.4f} | accuracy: {2:.1f}%\".format(\n",
        "            t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 00 | loss: 1.2232 | accuracy: 39.5%\n",
            "epoch: 20 | loss: 0.4105 | accuracy: 94.7%\n",
            "epoch: 40 | loss: 0.2419 | accuracy: 98.7%\n",
            "epoch: 60 | loss: 0.1764 | accuracy: 99.5%\n",
            "epoch: 80 | loss: 0.1382 | accuracy: 99.6%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "wHCvuSEaK-2x",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "_, pred_train = model(X_train, apply_softmax=True).max(dim=1)\n",
        "_, pred_test = model(X_test, apply_softmax=True).max(dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5whE6K0rOmGN",
        "colab_type": "code",
        "outputId": "0d895286-edda-4b8e-f520-ebb63d8fd070",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Train and test accuracies\n",
        "train_acc = get_accuracy(y_pred=pred_train, y_target=y_train)\n",
        "test_acc = get_accuracy(y_pred=pred_test, y_target=y_test)\n",
        "print (\"train acc: {0:.1f}%, test acc: {1:.1f}%\".format(train_acc, test_acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 99.6%, test acc: 96.8%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bzFb90SJOmI2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Visualization\n",
        "def plot_multiclass_decision_boundary(model, X, y):\n",
        "    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n",
        "    cmap = plt.cm.Spectral\n",
        "    \n",
        "    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n",
        "    y_pred = model(X_test, apply_softmax=True)\n",
        "    _, y_pred = y_pred.max(dim=1)\n",
        "    y_pred = y_pred.reshape(xx.shape)\n",
        "    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "    plt.xlim(xx.min(), xx.max())\n",
        "    plt.ylim(yy.min(), yy.max())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ViwfNFOYRDkm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We're going to plot a white point, which we know belongs to the malignant tumor class. Our well trained model here would accurately predict that it is indeed a malignant tumor!"
      ]
    },
    {
      "metadata": {
        "id": "_oEf6XRmOsJE",
        "colab_type": "code",
        "outputId": "34be9806-8b48-4c61-c5df-fe38a29ae6d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "cell_type": "code",
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gJQ4EAmFtVEXD1I9mUFsxBKkcr2D03CBcUZIp7RXi3dtfHByldI/B2zkM3B/d6zB2HTFf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W1rqPfsVOvsvncDUZHxJmMknzuPpmrkbz+gVaogimbfbiNn/3mOCubHiTcZsxTf3vgnb/EzYsRR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\nX0IIIYQQkhCo8CWEEEIIIQnh/wHnRsROLlFyjwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4f84a0da20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "o231eJaQPi5E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Great! We received great performances on both our train and test data splits. We're going to use this dataset to show the importance of data quality and quantity."
      ]
    },
    {
      "metadata": {
        "id": "pZ3rnGH8PtBu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Quality and Quantity"
      ]
    },
    {
      "metadata": {
        "id": "ONRP3WQgR3zc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's remove some training data near the decision boundary and see how robust the model is now."
      ]
    },
    {
      "metadata": {
        "id": "Sxd2S63EYtxt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Upload data from GitHub to notebook's local drive\n",
        "url = \"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/data/tumors_reduced.csv\"\n",
        "response = urllib.request.urlopen(url)\n",
        "html = response.read()\n",
        "with open(args.reduced_data_file, 'wb') as fp:\n",
        "    fp.write(html)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sU69PjH3Z4bm",
        "colab_type": "code",
        "outputId": "a9d507a7-9f96-4bb2-81ac-35485f8ca5f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "# Raw reduced data\n",
        "df_reduced = pd.read_csv(args.reduced_data_file, header=0)\n",
        "df_reduced.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>leukocyte_count</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>tumor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.472969</td>\n",
              "      <td>15.250393</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.805510</td>\n",
              "      <td>14.109676</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.834053</td>\n",
              "      <td>15.793920</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.572811</td>\n",
              "      <td>17.873286</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.633667</td>\n",
              "      <td>16.598559</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   leukocyte_count  blood_pressure  tumor\n",
              "0        13.472969       15.250393      1\n",
              "1        10.805510       14.109676      1\n",
              "2        13.834053       15.793920      1\n",
              "3         9.572811       17.873286      1\n",
              "4         7.633667       16.598559      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "1OwgEJSsZ4g5",
        "colab_type": "code",
        "outputId": "5464cb82-b7a7-4cc2-e137-27379e6d5c27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "cell_type": "code",
      "source": [
        "# Plot data\n",
        "plot_tumors(df_reduced)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nrAWBYE+TcvncW5pUczR0BUUA7x461Geh66jdH7XZ+K3Fwp0Wi+jE1QoIoX2R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\nUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4f84a0d4e0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "r9xlQme0beTY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Convert to PyTorch tensors\n",
        "X = df_reduced.as_matrix(columns=['leukocyte_count', 'blood_pressure'])\n",
        "y = df_reduced.as_matrix(columns=['tumor'])\n",
        "X = torch.from_numpy(X).float()\n",
        "y = torch.from_numpy(y.ravel()).long()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RerzDWJQbeVz",
        "colab_type": "code",
        "outputId": "98b02a78-e164-4f79-9492-054feab9d55b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Shuffle data\n",
        "shuffle_indices = torch.LongTensor(random.sample(range(0, len(X)), len(X)))\n",
        "X = X[shuffle_indices]\n",
        "y = y[shuffle_indices]\n",
        "\n",
        "# Split datasets\n",
        "test_start_idx = int(len(X) * args.train_size)\n",
        "X_train = X[:test_start_idx] \n",
        "y_train = y[:test_start_idx] \n",
        "X_test = X[test_start_idx:] \n",
        "y_test = y[test_start_idx:]\n",
        "print(\"We have %i train samples and %i test samples.\" % (len(X_train), len(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 540 train samples and 180 test samples.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JCZ7yDl1OsdU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=len(df_reduced.columns)-1, \n",
        "            hidden_dim=args.num_hidden_units, \n",
        "            output_dim=len(set(df_reduced.tumor)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-IZ4YOKtSCRk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7NBWLKDISDj8",
        "colab_type": "code",
        "outputId": "83cff604-b034-4aea-e4b3-47a713209b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "    \n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%20==0: \n",
        "        print (\"epoch: {0} | loss: {1:.4f} | accuracy: {2:.1f}%\".format(t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 0 | loss: 5.6444 | accuracy: 44.4%\n",
            "epoch: 20 | loss: 0.9575 | accuracy: 55.9%\n",
            "epoch: 40 | loss: 0.3089 | accuracy: 99.3%\n",
            "epoch: 60 | loss: 0.2096 | accuracy: 100.0%\n",
            "epoch: 80 | loss: 0.1302 | accuracy: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uGWbZlhUSFOz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "_, pred_train = model(X_train, apply_softmax=True).max(dim=1)\n",
        "_, pred_test = model(X_test, apply_softmax=True).max(dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Gz2Sh4JpSFT9",
        "colab_type": "code",
        "outputId": "8cb4c86b-0ff2-4548-a710-09883a8dc349",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Train and test accuracies\n",
        "train_acc = get_accuracy(y_pred=pred_train, y_target=y_train)\n",
        "test_acc = get_accuracy(y_pred=pred_test, y_target=y_test)\n",
        "print (\"train acc: {0:.1f}%, test acc: {1:.1f}%\".format(train_acc, test_acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 100.0%, test acc: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DmTCz8OnSFRn",
        "colab_type": "code",
        "outputId": "f3cf293c-b22b-49bf-d037-ca021376a5d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "cell_type": "code",
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AyRfBWUxwDYbhnRpteKzJbkP4zhPAnSdQTIqoZCXYw4G2jygubhjL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\n/B4ZtTgU7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7rc9OyLHNmlAu2aiuJRCM+GGLs13KzvdktDP6aVAaCWbZB+mmH8Tv+PLHEz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\n8g1Mn76R++STKIrYuXMnNm3ahM2bN6fdptTv/U//9E/x5JNP4le/+hVWr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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4f849cb208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kdP98xnlbvVn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This is a very scary but highly realistic scenario. Based on our reduced synthetic dataset, we have achieved a model that generalized really well on the test data. But when we ask for the prediction for the same white point earlier (which we known as a tumor), the prediction is now a benign tumor. We would have completely missed the tumor.\n",
        "\n",
        "**MODELS ARE NOT CRYSTAL BALLS** \n",
        "It's so important that before any machine learning, we really look at our data and ask ourselves if it is truly representative for the task we want to solve. The model itself may fit really well and generalize well on your data but if the data is of poor quality to begin with, the model cannot be trusted."
      ]
    },
    {
      "metadata": {
        "id": "yWzAC39adTwk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Models"
      ]
    },
    {
      "metadata": {
        "id": "cR45QpjQdY6N",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Once you are confident that your data is of good quality and quantity, you can finally start thinking about modeling. The type of model you choose depends on many factors, including the task, type of data, complexity required, etc.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/models1.png\" width=550>\n",
        "\n",
        "So once you figure out what type of model your task needs, start with simple models and then slowly add complexity. You don’t want to start with neural networks right away because that may not be right model for your data and task. Striking this balance in model complexity is one of the key tasks of your data scientists. **simple models → complex models**"
      ]
    }
  ]
}
